1. Field of the Invention
The present invention relates to an apparatus and method for making a medical diagnosis by discriminating attribution degrees for determining to which of multiple disease groups, test data of a given patient is attributed through multiple-group discriminant analysis by integrating the results of two-group linear discriminant analysis. Such an apparatus and method are useful, for example, for diagnosing diseases (or for determining to which of multiple disease groups the patient belongs).
2. Description of the Related Art
The two-group linear discriminant analysis is one of the most popular methods for statistically determining to which of two disease groups a given patient belongs. The two-group linear discriminant analysis provides a highly dense information from the viewpoint of knowledge acquisition because the two-group linear discriminant analysis allows determining to which of two disease groups the patient belongs by determining whether two group-linear discriminant function assumes a positive value or a negative value.
On the other hand, examples of approaches for determining one group out of probable multigroups with respect to given disease data (diagnosis systems) include an approach represented by an expert system by which the disease group is determined with coded information of knowledge and an inference engine, an approach using the learning of a neural network, and an approach for inductively determining the disease group in a multiple-group discriminant analysis as an application of multivariate analyses.
A process by which an experienced medical specialist, for example, selects one disease group from the rest of multiple disease groups is as follows. The medical specialist will never select one group at one time, but will rather select several probable groups from multiple disease groups to finally select the most probable group through comparison of the probable groups. In general, this process is referred to as "preferable selection process" in which humans exhibit a relatively high ability.
With respect to the recognition of multiple group patterns as mentioned above, the Journal of Electronic Information and Communications Society A, Vol.J 72-A, No.1, pp.41-48, January, 1989 discloses a multiple group pattern recognition by pair discrimination.
In this multiple group pattern recognition, two groups are discriminated from each other with respect to all pairs to produce the discrimination result for multiple groups by integrating the result of the two group discrimination. The multiple pattern recognition presumes a variance-covariance matrix not common for all groups, but separate for each of the pairs. Then the multiple pattern recognition calculates the squares D.sub.i.sup.2 and D.sub.j.sup.2 of Mahalanobis' generalized distances between an unknown input pattern x and each of two groups i and j, and posteriori probabilities P (.PI.i.vertline.x) and P(.PI.j.vertline.x). The two group discrimination results are obtained by a comparison between the squares D.sub.i.sup.2 and D.sub.j.sup.2 of Mahalanobis' generalized distances and between the posteriori probabilities P(.PI.i.vertline.x) and P(.PI.j.vertline.x). After these results are normalized (to obtain normalized pair statistics) and integrated with each other, the final discrimination result is obtained for the unknown input pattern x. However, the Journal describes no specific method for calculating the posteriori probabilities P(.PI.i.vertline.x) and P (.PI.j.vertline.x).
Furthermore, the Journal describes three methods for obtaining the final discrimination result, a decision by majority, a minimax method and an approach by expectation values. The decision by majority is a method in which a solution is decided by majority on integrated results of two-group discriminant analysis. The minimax approach is a method in which the maximum value of the Mahalanobis' generalized distances is calculated between the unknown pattern and group, and, the unknown pattern is excluded from the group which has the maximum value. This method is effective when the number of groups are large. The approach by expectation values is a method in which an expectation value of a normalized pair statistic is calculated for a group .PI.i to decide a solution which provides the best result.
In such conventional multiple-group discriminant analysis, however, the quality of data used for knowledge acquisition (e.g., for the calculation of a linear discriminant function) often affects the reliability of the obtained linear discriminant function, for example, when a system is actually constructed for diagnosing each case of disease.
In the actual diagnosis of disease using two group discrimination discriminant analysis, some combinations of two groups are significant, while others are not. Therefore, it is not preferable to equally treat all the combinations of two groups in two-group discriminant analysis. Medical specialists seem to account for the difference in the significance of combinations of two groups based on empirical knowledge.
The foregoing points are very important for actual systems of discriminating multiple groups, especially for systems of diagnosing diseases.